The super short version: We validated and refined our motivations model based on data from 30,000 gamers around the world.
If you’re a gamer and haven’t taken the Gamer Motivation Profile yet, consider doing so before reading about the model.
We developed the Gamer Motivation Profile such that data collected over time could be used to test new motivations, try out new features, and refine the backend algorithms. Since we launched the profile tool, over 30,000 gamers around the world have taken it. The size and geographic scope of this data has allowed us to validate and refine our motivation model.
The initial version of the Gamer Motivation Profile allowed us to collect large samples of English-speaking gamers from many geographic regions (see detailed sample notes):
- US + Canada: 6,222
- Brazil: 6,044
- Indonesia: 6,000
- Philippines: 3,198
- Singapore: 2,316
- EU: 2,004
- Australia: 660
- East Asia: 510
- (long tail of other countries)
We are continually testing out new items in the profile tool. Thus, interspersed within the survey inventory are additional items being tested to improve existing scales or to explore factors that are suggested by theory or gamer input. As we did in developing the original model, we conducted a factor analysis of the inventory items. We conducted a factor analysis separately on the US + Canada, Indonesia, and EU data. The number of factors that emerged and the factor composition across these 3 regions were identical. The same results also emerged when a factor analysis was applied to the full data set.
The number of factors that emerged and the factor composition across these 3 regions were identical.
To be clear, this doesn’t mean that there were no geographical differences in motivations, only that the factor structure was the same across regions. Or put another way, everyone recognizes that there is a set of cooked dishes which are spicy even though preference and tolerance for spicy foods vary a great deal from person to person.
Visualizing How The Factors are Related
We identified 12 motivation factors. To understand and visualize how these factors cluster and relate to each other, we used hierarchical clustering. In brief, this clustering method starts every variable on its own, and then merges the most similar variables together into clusters; the algorithm continues merging clusters until everything is in one cluster. The visual output is called a dendrogram and graphically shows how our motivation factors are related: the earlier that two variables merge, the more closely related they are.
The dendrogram reveals an interesting hierarchical pattern among the 12 motivations. First, there are 6 pairs of motivations that are closely related. These are largely the same clusters as in the earlier version of the model, with the addition of the Discovery factor. And then these clusters fall into 3 high-level groups.
Our Gamer Motivations Framework
First, we’ll describe the 12 motivation factors and how they cluster together.
Immersion: Gamers with high Immersion scores want games with interesting narratives, settings, and customization options so they can be deeply immersed in the alternate worlds created by games. Gamers with low Immersion scores are more grounded in the gameplay mechanics and care less about the narrative experiences that games offer. Immersion is composed of 3 underlying motivations:
- Fantasy: The desire to become someone else, somewhere else.
- Story: The importance of an elaborate storyline and interesting characters.
Creativity: Gamers with high Creativity scores are constantly experimenting with their game worlds and tailoring them with their own designs and customizations. Gamers with low Creativity scores are more practical in their gaming style and accept their game worlds as they are.
- Design: The appeal of expression and deep customization.
- Discovery: The desire to explore, tinker, and experiment with the game world.
Action: Gamers with high Action scores are aggressive and like to jump in the fray and be surrounded by dramatic visuals and effects. Gamers with low Action scores prefer slower-paced games with calmer settings. Action is composed of two underlying motivations:
- Destruction: The enjoyment of chaos, mayhem, guns, and explosives.
- Excitement: The enjoymen